{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e39fbeeb-1bd5-49b3-8696-1e1134f1ccc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "思路：\n",
    "1、数据集读取,数据处理及训练集和测试集划分\n",
    "2、选取不同分布的朴素贝叶斯模型进行分类模型训练与测试、评价\n",
    "\"\"\"\n",
    "import pandas as pd\n",
    "import warnings\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer  # 文本型数据处理\n",
    "from sklearn.preprocessing import LabelEncoder  # 字符串型数据编码\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.naive_bayes import GaussianNB, BernoulliNB, MultinomialNB\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "\n",
    "from matplotlib import rcParams\n",
    "import matplotlib\n",
    "# matplotlib.use(\"Agg\")  # 输出时不显示绘图\n",
    "import matplotlib.pyplot as plt  # matplotlib.use('agg')必须在本句执行前运行\n",
    "\n",
    "rcParams['font.family'] = 'simhei'  # 可以让图像中显示中文（黑体），无需引用\n",
    "rcParams['axes.unicode_minus'] = False  # 解决负数坐标显示问题\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# #############################################################################################\n",
    "# 公共部分：分类模型评价体系evaluation\n",
    "# todo: 构建分类模型的评价体系并存储在evaluation中，方便对比查看，全局变量\n",
    "evaluation = pd.DataFrame({'Model': [],\n",
    "                           '准确率': [],\n",
    "                           '精确率': [],\n",
    "                           '召回率': [],\n",
    "                           'F1 值': [],\n",
    "                           'AUC值': [],\n",
    "                           '5折交叉验证的score': []})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d2574272-629a-4775-bd0e-64a925bf8192",
   "metadata": {},
   "outputs": [],
   "source": [
    "# #############################################################################################\n",
    "# 步骤1 todo: 数据读取、单词文本处理及标签数值化处理、数据集划分\n",
    "def data_preprocess(train_size):\n",
    "    # 步骤1.1 todo: 读取数据并统计数据类别信息\n",
    "    df = pd.read_csv('./data/SMSSpamCollection', delimiter='\\t', header=None)  # 利用pandas直接读取已有数据，数据以tab分割\n",
    "    print(df.describe())  # 了解数据的基本信息\n",
    "    print('为spam短信数量：', df[df[0] == 'spam'][0].count())\n",
    "    print('为ham短信数量：', df[df[0] == 'ham'][0].count())\n",
    "\n",
    "    # 步骤1.2 todo: 对数据集中单词文本进行处理\n",
    "    # 由于数据集为单词文本数据，构建TfidfVectorizer来计算每个单词的TF-IDF权重\n",
    "    tfidf_vectorizer = TfidfVectorizer()\n",
    "    X = tfidf_vectorizer.fit_transform(df[1])\n",
    "    X = X.toarray()  # 将数据矩阵转化为数组\n",
    "    voc = tfidf_vectorizer.get_feature_names_out()  # 构建的词汇表\n",
    "    print(len(voc))\n",
    "\n",
    "    # 步骤1.3 todo: 目标变量中为字符串型数据，使用labelEncoder处理（spam-1，ham-0）\n",
    "    le = LabelEncoder()  # 对定型特征多值化\n",
    "    y = le.fit_transform(df[0])\n",
    "\n",
    "    # 步骤1.4 todo: 对整体数据按照train_size进行划分，得到训练集和测试集, random_state确保结果的一致性\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=train_size, random_state=0)\n",
    "\n",
    "    return X_train, y_train, X_test, y_test, df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "31ed380a-6735-4927-b045-b9deea9c9c22",
   "metadata": {},
   "outputs": [],
   "source": [
    "# #############################################################################################\n",
    "# 步骤2 todo: 进行朴素贝叶斯分类模型训练与测试、评价\n",
    "# 由于选取的不同分布的朴素贝叶斯分类器，写成函数形式，便于引用\n",
    "def naive_bayes_with_diff_distribution(model, model_name, X_train, y_train, X_test, y_test):\n",
    "    # 步骤2.1 todo: 调用不同分布的朴素贝叶斯分类实例，进行训练测试\n",
    "    NB_model = model\n",
    "    NB_model.fit(X_train, y_train)\n",
    "    y_test_predict = NB_model.predict(X_test)\n",
    "\n",
    "    # 步骤2.2 todo: 计算分类评价指标：测试集的准确率accuracy、精确率precision、召回率recall和综合评价指标 F1 值\n",
    "    # 精确率是指分类器预测出的垃圾短信中真的是垃圾短信的比例\n",
    "    # 召回率是所有真的垃圾短信被分类器正确找出来的比例\n",
    "    # F1 值是精确率和召回率的调和均值\n",
    "    acc_test = accuracy_score(y_test, y_test_predict)  # 和模型自带的score一致\n",
    "    precision_test = precision_score(y_test, y_test_predict)\n",
    "    recall_test = recall_score(y_test, y_test_predict)\n",
    "    f1score_test = f1_score(y_test, y_test_predict)\n",
    "\n",
    "    # 步骤2.3 todo: 绘制ROC曲线，计算auc，度量分类模型的预测能力\n",
    "    # ROC曲线以召回率为纵轴，以假正例率为横轴，ROC曲线下的面积为AUC值\n",
    "    y_test_predict_proba = NB_model.predict_proba(X_test)\n",
    "    false_positive_rate, recall, thresholds = roc_curve(y_test, y_test_predict_proba[:, 1])\n",
    "    roc_auc = auc(false_positive_rate, recall)  # 计算auc的值\n",
    "    plt.figure()\n",
    "    plt.title('%s 模型的 ROC-AUC 图' % model_name)\n",
    "    plt.plot(false_positive_rate, recall, 'r', label='AUC = %0.3f' % roc_auc)\n",
    "    plt.legend(loc='best')\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.0])\n",
    "    plt.ylabel('真正例率(召回率)')\n",
    "    plt.xlabel('假正例率')\n",
    "    plt.savefig('./results/ROC_AUC_with_model_{}.png'.format(model_name))\n",
    "    plt.show()\n",
    "\n",
    "    # 步骤2.4 todo: 计算测试集的5折交叉验证的score\n",
    "    cv_test = float(format(cross_val_score(NB_model, X_test, y_test, cv=5).mean(), '.3f'))\n",
    "\n",
    "    # 步骤2.5 todo: 将朴素贝叶斯分类模型计算的相关评价信息存入evaluation中\n",
    "    r = evaluation.shape[0]\n",
    "    evaluation.loc[r] = ['{}分类模型'.format(model_name), acc_test,\n",
    "                         precision_test, recall_test, f1score_test, roc_auc, cv_test]\n",
    "\n",
    "    # 步骤2.6 todo: 将评价指标写入csv文件中，便于查看\n",
    "    evaluation.to_csv('./results/evaluation.csv', sep=',', header=True, index=True,\n",
    "                      encoding='utf_8_sig')  # encoding防止中文乱码\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e703f7ce-d0ef-415e-8c28-069428ab7e54",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           0                       1\n",
      "count   5572                    5572\n",
      "unique     2                    5169\n",
      "top      ham  Sorry, I'll call later\n",
      "freq    4825                      30\n",
      "为spam短信数量： 747\n",
      "为ham短信数量： 4825\n",
      "8713\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型测试结束！\n"
     ]
    }
   ],
   "source": [
    "# #############################################################################################\n",
    "# 程序入口\n",
    "if __name__ == '__main__':\n",
    "    # 步骤1 todo: 读取数据集,并给出训练集和测试集\n",
    "    train_size = 0.67\n",
    "    X_train, y_train, X_test, y_test, df = data_preprocess(train_size)\n",
    "\n",
    "    # 步骤2 todo: 进行朴素贝叶斯分类模型训练与测试、评价\n",
    "    # 'GaussianNB':Gauss朴素贝叶斯\n",
    "    # 'BernoulliNB':伯努利朴素贝叶斯\n",
    "    # 'MultinomialNB':多项式朴素贝叶斯\n",
    "    models = [GaussianNB(), BernoulliNB(), MultinomialNB()]\n",
    "    model_names = ['GaussianNB', 'BernoulliNB', 'MultinomialNB']\n",
    "    for i, model in enumerate(models):\n",
    "        naive_bayes_with_diff_distribution(model, model_names[i], X_train, y_train, X_test, y_test)\n",
    "\n",
    "    print('模型测试结束！')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1641300c-ec40-4d68-b728-2752d15d8586",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
